Abstract
Neonatal sepsis is characterized by the system’s extreme response to an infection and persists as one of the biggest life-threatening diseases. The gold standard treatment is administrating an antibiotic, which, unfortunately, is often made too late. The diagnosis should be easier, faster, and achieved through non-invasive methods. Recently, entropy, a non-linear feature, has been applied to different physiological signals to detect diseases having very promising results. In this study, several entropy measures were applied to the breathing cycle duration (TTot) of the respiratory signals for 20 neonates. In total, 18 distinct methods of entropy were initially applied to 30-minute segments. Using Spearman’s correlation, it was detected strong correlation similarities between some of the measures. On the other hand, bubble, attention, phase, and spectral entropies were negatively correlated with all the other measures. To detect the presence of Sepsis, the slope of the multiscale entropy index was analyzed. Also, a changing point in the slope was probed, when possible, and then was applied linear regression to two subsets of data, before and after the changing point. Effectively, the Wilcoxon Sign Rank Test showed that the results for the total slope of the Sample, Corrected Conditional, Distribution, Permutation, Fuzzy, Gridded Distribution, Incremental, and Entropy of Entropy were statistically significant to infer that entropy decreases with time. Nonetheless, further work should confirm these results with a larger dataset that includes healthy and pathological neonates. © 2022 IEEE.

Abstract
Reasoning about spatial information is fundamental in natural language to fully understand relationships between entities and/or between events. However, the complexity underlying such reasoning makes it hard to represent formally spatial information. Despite the growing interest on this topic, and the development of some frameworks, many problems persist regarding, for instance, the coverage of a wide variety of linguistic constructions and of languages. In this paper, we present a proposal of integrating ISO-Space into a ISO-based multilayer annotation scheme, designed to annotate news in European Portuguese. This scheme already enables annotation at three levels, temporal, referential and thematic, by combining postulates from ISO 24617-1, 4 and 9. Since the corpus comprises news articles, and spatial information is relevant within this kind of texts, a more detailed account of space was required. The main objective of this paper is to discuss the process of integrating ISO-Space with the existing layers of our annotation scheme, assessing the compatibility of the aforementioned parts of ISO 24617, and the problems posed by the harmonization of the four layers and by some specifications of ISO-Space. © European Language Resources Association (ELRA).

Abstract
This article proposes a Markov Chain Monte Carlo (MCMC) rendering algorithm based on a family of guided transition kernels. The kernels exploit properties of ensembles of light transport paths, which are distributed according to the lighting in the scene, and utilize this information to make informed decisions for guiding local path sampling. Critically, our approach does not require caching distributions in world space, saving time and memory, yet it is able to make guided sampling decisions based on whole paths. We show how this can be implemented efficiently by organizing the paths in each ensemble and designing transition kernels for MCMC rendering based on a carefully chosen subset of paths from the ensemble. This algorithm is easy to parallelize and leads to improvements in variance when rendering a variety of scenes.

Abstract
Artificial intelligence techniques are now being applied in different medical solutions ranging from disease screening to activity recognition and computer-aided diagnosis. The combination of computer science methods and medical knowledge facilitates and improves the accuracy of the different processes and tools. Inspired by these advances, this paper performs a literature review focused on state-of-the-art glaucoma screening, segmentation, and classification based on images of the papilla and excavation using deep learning techniques. These techniques have been shown to have high sensitivity and specificity in glaucoma screening based on papilla and excavation images. The automatic segmentation of the contours of the optic disc and the excavation then allows the identification and assessment of the glaucomatous disease's progression. As a result, we verified whether deep learning techniques may be helpful in performing accurate and low-cost measurements related to glaucoma, which may promote patient empowerment and help medical doctors better monitor patients.

Abstract
Applying machine learning (ML), and especially deep learning, to understand visual content is becoming common practice in many application areas. However, little attention has been given to its use within the multimedia creative domain. It is true that ML is already popular for content creation, but the progress achieved so far addresses essentially textual content or the identification and selection of specific types of content. A wealth of possibilities are yet to be explored by bringing the use of ML into the multimedia creative process, allowing the knowledge inferred by the former to influence automatically how new multimedia content is created. The work presented in this article provides contributions in three distinct ways towards this goal: firstly, it proposes a methodology to re-train popular neural network models in identifying new thematic concepts in static visual content and attaching meaningful annotations to the detected regions of interest; secondly, it presents varied visual digital effects and corresponding tools that can be automatically called upon to apply such effects in a previously analyzed photo; thirdly, it defines a complete automated creative workflow, from the acquisition of a photograph and corresponding contextual data, through the ML region-based annotation, to the automatic application of digital effects and generation of a semantically aware multimedia story driven by the previously derived situational and visual contextual data. Additionally, it presents a variant of this automated workflow by offering to the user the possibility of manipulating the automatic annotations in an assisted manner. The final aim is to transform a static digital photo into a short video clip, taking into account the information acquired. The final result strongly contrasts with current standard approaches of creating random movements, by implementing an intelligent content- and context-aware video.

Abstract
Professional theatre actors are highly specialized in controlling their own expressive behaviour and non-verbal emotional expressiveness, so they are of particular interest in fields of study such as affective computing. We present Acting Emotions, an experimental protocol to investigate the physiological correlates of emotional valence and arousal within professional theatre actors. Ultimately, our protocol examines the physiological agreement of valence and arousal amongst several actors. Our main contribution lies in the open selection of the emotional set by the participants, based on a set of four categorical emotions, which are self-assessed at the end of each experiment. The experiment protocol was validated by analyzing the inter-rater agreement (> 0.261 arousal, > 0.560 valence), the continuous annotation trajectories, and comparing the box plots for different emotion categories. Results show that the participants successfully induced the expected emotion set to a significant statistical level of distinct valence and arousal distributions. © 2022 Owner/Author.